There are several popular deep learning frameworks that are widely used by researchers and developers. Some of the most popular ones include:

  1. TensorFlow: Developed by Google, TensorFlow is an open-source framework that provides a comprehensive ecosystem of tools, libraries, and resources for building and deploying deep learning models. It supports a wide range of platforms and offers various APIs for different levels of abstraction.

  2. PyTorch: Developed by Facebook's AI Research team, PyTorch is an open-source deep learning framework that emphasizes simplicity and ease of use. It offers a dynamic computation graph and provides a smooth transition between research prototyping and production deployment.

  3. Keras: Built on top of TensorFlow, Keras is an open-source deep learning library that offers a user-friendly interface and high-level abstractions. Its modular design allows for easy model building, and it supports various backends, including TensorFlow and Microsoft Cognitive Toolkit (CNTK).

  4. Caffe: Developed by Berkeley AI Research and community contributors, Caffe is a deep learning framework known for its expressive architecture and fast computation. It focuses on speed and efficiency, making it suitable for large-scale deployments.

  5. MXNet: MXNet is a flexible and efficient deep learning framework developed by Apache. It offers support for distributed computing, allowing users to train and deploy models on multiple devices and platforms.

  6. Theano: Theano is a Python library that allows for efficient mathematical computations, especially involving multi-dimensional arrays. It is widely used for deep learning research but is no longer actively developed since its main contributors have shifted to developing other frameworks.

  7. Microsoft Cognitive Toolkit (CNTK): Developed by Microsoft, CNTK is an open-source deep learning framework that offers excellent scalability and performance. It supports various programming languages and provides both high-level and low-level APIs.

  These frameworks provide a wide range of capabilities and cater to different needs and preferences. The choice of framework often depends on factors such as the application requirements, availability of resources and libraries, ease of use, and community support.